Face animation synthesis

ABSTRACT

A methodology for training a machine learning model to generate color-neutral input face images is described. For each training face image from a training dataset that is used for training the model, the training system generates an input face image, which has the color and lighting of a randomly selected image from the set of color source images, and which has facial features and expression of a face object from the training face image. Because, during training, the machine learning model is “confused” by changing the color and lighting of a training face image to a randomly selected different color and lighting, the trained machine learning model generates a color neutral embedding representing facial features from the training face image.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/107,410, filed on Nov. 30, 2020, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to manipulating electroniccontent.

BACKGROUND

Face animation synthesis is a process that may include transferring afacial expression of a face from a source image (e.g., a frame in asource video) to a face in a target image. While there are existingtechniques for face animation synthesis, there is a considerable roomfor improvement in the area of making the result of face animationsynthesis, on one hand, true to the facial expression in a source imageand, on the other hand, true to the identity features of the face fromthe target image. Applications of face animation synthesis may bebeneficial in entertainment shows, computer games, video conversations,as well as in providing augmented reality experiences in a messagingsystem.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some embodiments are illustratedby way of example, and not limitation, in the figures of theaccompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome examples.

FIG. 6 is a flowchart for providing an augmented reality experienceutilizing face animation synthesis, in accordance with some examples.

FIG. 7 illustrates an example of an image depicting a user, an imagedepicting an actor, and an image resulting from a face animationsynthesis process.

FIG. 8 illustrates example of a set of facial landmarks that identifyrespective positions of facial features that determine a facialexpression.

FIG. 9 is a diagrammatic representation of a camera view user interfacedisplaying the output of a digital image sensor of the camera and amodified source frame, in accordance with some examples.

FIG. 10 is a diagrammatic representation of a camera view user interfacedisplaying a modified source frame instead of the output of a digitalimage sensor of the camera, in accordance with some examples.

FIG. 11 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

DETAILED DESCRIPTION

Embodiments of the present disclosure improve the functionality ofelectronic messaging software and systems by enhancing users' experienceof engaging with augmented reality technology.

In some embodiments, the users' experience of engaging with augmentedreality technology may be enhanced by providing a process, referred toas face animation synthesis, that replaces an actor's face in the framesof a video with a user's face from the user's portrait image, such thatthe resulting face in the frames of the video retains the facialexpressions and color and lighting of the actor's face but has thelikeness of the user's face. An example of an image depicting a user, animage depicting an actor, and an image resulting from a face animationsynthesis process is illustrated in FIG. 7, which is described furtherbelow. In some embodiments, the process of face animation synthesisemploys machine learning technology, e.g., convolutional neuralnetworks.

A first neural network, an embedder machine learning model, isconfigured to generate, based on an image comprising a target faceobject, an embedding that represents facial features of the target faceobject. The word “target” is used because the target face object can bedescribed as the target, onto which a facial expression of anotherperson is projected. Facial features represented by an embedding includecharacteristics that make a human face recognizable as a specificperson, regardless of an associated facial expression and regardless ofthe color and lighting of the face. Examples of facial features includerespective sizes and shapes of the eyes, the nose, the mouth, eyebrows,as well as features like wrinkles and beards. It may be said that theembedding represents the likeness of a person depicted by the faceobject. The face object is obtained from a portrait image of a user,e.g., from a user's selfie. A face object may be derived from an imageby means of face detection technology, utilizing, for example,Viola-Jones feature based object detection framework or MTCNN(Multi-Task Cascaded Convolutional Neural Network). The embedding, inone example, is generated by the embedder machine learning model in theform of a numerical tensor.

Another neural network, termed a generator machine learning model, isconfigured to blend a user's face from the user's portrait image with aface in the frames of a video (e.g., with an actor's face in a shortvideo clip from a movie), such that the resulting face in the frames ofthe video retains the facial expressions of the actor's face but has thelikeness of the user's face. The video is referred to as a source video,because it can be understood to be the source of the facial expressionand the background scenery for the user from the portrait image.

The input to the generator machine learning model is the embedding,produced by the embedder machine learning model based on the portraitimage of a user and the frames of the source video. In the source videothat is provided to the generator machine learning model as input, theface area (the area between the jawline and the eyebrows) in each frameis obscured, with the exception of the area depicting the interior ofthe actor's mouth in those frames where the actor's mouth is open.Additionally, the generator machine learning model receives, as input, arepresentation of the actor's facial expression in each frame. Thefacial expression is encoded by a set of facial landmarks that identifyrespective positions of facial features that determine a facialexpression, such as, for example, the position and orientation of themouth and eyebrows, the position and orientation of the pupils thatdetermine the direction of the gaze. An example of a set of faciallandmarks that identify respective positions of facial features thatdetermine a facial expression is illustrated in FIG. 8, which isdescribed further below. A set of facial landmarks for a face object canbe generated by a pre-trained landmark detection model. The output ofthe generator machine learning model is the frames of the video, inwhich the face of the actor has the likeness of the user while retainingits original facial expressions and the color and lighting.

The generator machine learning model can be trained using a trainingdataset of videos of people talking (videos of people answeringquestions, as in an interview, for example). During the training, thegenerator machine learning model neural network receives inputs in theform of different frames from the same video, where the frames depictthe same person (e.g., an actor). The input frames depicting an actorare modified to retain features that determine a facial expression ofthe actor (e.g., the position and orientation of the mouth and eyebrows,the position and orientation of the pupils that determine the directionof the gaze and so on), while removing facial features that determinethe unique identity of the actor (e.g., the shape and size of the eyes,the distance between the eyes, the shape and size of the mouth, and soon.) A facial expression depicted in an image may be encoded in the formof a set of landmarks that indicate features such as the position andorientation of the mouth and eyebrows, the position and orientation ofthe pupils that determine the direction of the gaze, and so on.

The embedder machine learning model, like the generator machine learningmodel, is trained using videos of people talking, where the embeddermachine learning model neural network receives inputs in the form of theframes from a video that depicts the same person. However, because theoutput of the embedder machine learning model—the embedding—is used bythe generator machine learning model to produce frames of the video, inwhich the face of an actor has the facial features represented by theembedding while retaining not only the facial expressions of the actorbut, also, the color and lighting of the actor's face, the embeddermachine learning model is trained to not include the color and lightingof the target face in the features of the resulting embedding. Variousprocessing modules utilized in training the generator machine learningmodel and the embedder machine learning model, as well as in generatingand/or preparing training data, is referred to as a training system, forthe purposes of this description.

In order to train the embedder machine learning model to produce theembedding representing facial features without the color and lighting ofthe face, the training system utilizes a set of color source images thatdepict faces of different people that have different respective facecolor and lighting. For each training face image from a training datasetthat is used for training the embedder machine learning model, thetraining system generates an input face image, which has the color andlighting of a randomly selected image from the set of color sourceimages, and which has facial features and expression of a face objectfrom the training face image. Because, during training, the embeddermachine learning model is deliberately “confused” by changing the colorand lighting of a training face image to a randomly selected differentcolor and lighting, the trained embedder machine learning modelgenerates a color neutral embedding representing facial features fromthe training face image.

An example process for transferring color and lighting from a randomlyselected image from the set of color source images onto the target faceimage comprises generating or accessing respective facial landmarks forthe target face image and the color source image and warping the colorsource image in such a way that its landmarks match the landmarks fromthe target face, so that the respective face contours, eyebrows, eyes,noses and mouths in the color source image and the target face arealigned. Other operations include computing respective Laplacian pyramidrepresentations for each image (the color source image and the targetface image). In one example, the size of the image at the smallestpyramid level is 1/16 resolution. The smallest level in the pyramidrepresenting the target face image is replaced with the smallest levelof the pyramid representing the warped color source image to produce amodified pyramid. The image restored from the modified pyramid is usedas the input face image, as it has the facial features and expressionfrom the target face but the color and lighting from the color sourceimage. The resulting input image is a random-color version of the targetimage.

The trained embedder machine learning model and the generator machinelearning model may be used in the process of face animation synthesis asfollows. The embedder machine learning model uses, as input, a portraitimage comprising a target face object as input, and generates anembedding. The embedding represents facial features from the target faceobject and lacks representation of color and lighting of the target faceobject. The generator machine learning model uses, as input, a sourceframe comprising a source face object representing an actor, andmodifies the source frame by replacing the source face object with a newface object, where the new face object has the facial featuresrepresented by the embedding, has a facial expression from the sourceface object, and has color and lighting from the source face object. Themodified source frame may be displayed on a display device. The faceanimation synthesis methodologies described herein may be madeaccessible to users of a messaging system.

A messaging system that hosts a backend service for an associatedmessaging client is configured to permit users to capture images andvideos with a camera, provided with a client device that hosts themessaging client, and to share the captured content with other users viaa network communication. The messaging system is also configured toprovide augmented reality (AR) components accessible via the messagingclient. AR components can be used to modify content captured by acamera, e.g., by overlaying pictures or animation on top of the capturedimage or video frame, or by adding three-dimensional (3D) effects,objects, characters, and transformations. An AR component may beimplemented using a programming language suitable for app development,such as, e.g., JavaScript or Java. The AR components are identified inthe messaging server system by respective AR component identifiers.

A user can access functionality provided by an AR component by engaginga user-selectable element included in a camera view user interfacepresented by the messaging client. A camera view user interface isconfigured to display the output of a digital image sensor of a cameraprovided with an associated client device, to display a user selectableelement actionable to capture an image by the camera or to start andstop video recording, and also to display one or more user selectableelements representing respective AR components. The camera view userinterface may include one or more user selectable elements that permit auser to apply and also to remove the visual effect produced by the ARcomponent. A screen displayed by the messaging client, which can includethe output of a digital image sensor of a camera, a user selectableelement actionable to capture an image by the camera or to start andstop video recording, and also can display one or more user selectableelements representing respective AR components is referred to as acamera view screen. A user selectable element representing an ARcomponent is actionable to launch the AR component. When the ARcomponent is launched, the output of a digital image sensor of a cameradisplayed in the camera view user interface is augmented with themodification provided by the AR component. For example, an AR componentcan be configured to detect the head position of the person beingcaptured by the digital image sensor and overlay an image of a party hatover the detected head position, such that the viewer would see theperson presented as wearing the party hat.

An example AR component, which is configured to provide face animationsynthesis capability, may be referred to as a face animation synthesisAR component for the purposes of this description. The face animationsynthesis AR component can be made available to a user by providing auser selectable element, that represents the face animation synthesis ARcomponent, in the camera view user interface presented by the messagingclient. When a user, while accessing the messaging client, engages theuser selectable element representing the face animation synthesis ARcomponent in the camera view user interface, the messaging system loadsthe AR component in the messaging client. The loaded face animationsynthesis AR component accesses a portrait image of the user (the userwho is accessing the messaging client or a user different from the userwho is accessing the messaging client), accesses a source frame, andexecutes the face animation synthesis process described above. Theresult of the face animation synthesis process, a source frame modifiedsuch that the face depicted in the modified source frame has thelikeness of the face from the portrait image, but the facial expressionand the color and lighting from the source frame, is presented in thecamera view user interface of the messaging client. In some examples,the face animation synthesis AR component performs the face animationsynthesis process with respect to the frames of a source video togenerate a modified source video, in which the actor's face in the videoappears to perform the same facial movements as the actor's face in thesource video, but has the likeness of the face from the portrait image.

The result of the face animation synthesis process can be displayed inthe camera view user interface as overlaid over a portion of the outputof a digital image sensor of the camera. For example, in the case wherea user is using a front facing camera such that the output of thedigital image sensor of the camera is the image of the user, the camerascreen view displays the image of the user captured by the digital imagesensor of the camera and, also, the modified source video with the facefrom the portrait image.

In another example, the modified source video with the face from theportrait image is presented on the camera view screen instead of theoutput of a digital image sensor of the camera. In this example, theoutput of a digital image sensor of the camera is not visible in thecamera view screen.

It will be noted that, while the face animation synthesis process isbeing described in the context of a messaging system, the methodologiesdescribed herein can be used beneficially in various other environments,such as entertainment shows, computer games, video conversations and soon.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104. Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

A messaging client 104 is able to communicate and exchange data withanother messaging client 104 and with the messaging server system 108via the network 106. The data exchanged between messaging client 104,and between a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data). Forexample, the messaging client 104 permits a user to access functionalityprovided by the face animation synthesis AR component (namely, replacingan actor's face in the frames of a video with a user's face from theuser's portrait image, such that the resulting face in the frames of thevideo retains the facial expressions of the actor's face but has thelikeness of the user's face), which may reside, at least partially, atthe messaging server system 108. As explained above, the face animationsynthesis AR component configured to provide face animation synthesiscapability.

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity. For example, with respect to the functionality provided by theface animation synthesis AR component, the operations of generating anembedding representing facial features from a target face object andexecuting a generator machine learning model, using the embedding andthe source frame as input, to modify the source frame by replacing thesource face object with a new face object that included the facialfeatures represented by the embedding, a facial expression from thesource face object, and color and lighting from the source face object,which may be performed in response to detecting activation of the userselectable element representing the face animation synthesis ARcomponent, may be executed at the messaging server system 108 in orderto conserve resources of the client device 102 hosting messaging client104. Alternatively, if it is determined that the client device 102hosting messaging client 104 has sufficient processing resources, someor all of these operations may be executed by the messaging client 104.In some examples, the training of the embedder machine learning modeland the training of the generator machine learning model may beperformed at the messaging server system 108.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104. For example, the messaging client 104 can present a camera viewuser interface that displays the output of a digital image sensor of acamera provided with the client device 102, and also to display a userselectable element actionable to load the face animation synthesis ARcomponent in the messaging client 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, application servers 112. Theapplication servers 112 are communicatively coupled to a database server118, which facilitates access to a database 120 that stores dataassociated with messages processed by the application servers 112.Similarly, a web server 124 is coupled to the application servers 112,and provides web-based interfaces to the application servers 112. Tothis end, the web server 124 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 112. The Application Program Interface (API) server110 exposes various functions supported by the application servers 112,including account registration, login functionality, the sending ofmessages, via the application servers 112, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 114, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 112 host a number of server applications andsubsystems, including for example a messaging server 114, an imageprocessing server 116, and a social network server 122. The messagingserver 114 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor and memory intensive processing ofdata may also be performed server-side by the messaging server 114, inview of the hardware requirements for such processing.

The application servers 112 also include an image processing server 116that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 114. Some of thevarious image processing operations may be performed by various ARcomponents, which can be hosted or supported by the image processingserver 116. An example of an AR component, as discussed above, is theface animation synthesis AR component, which is configured to replace anactor's face in the frames of a video with a user's face from the user'sportrait image, such that the resulting face in the frames of the videoretains the facial expressions of the actor's face but has the likenessof the user's face.

The social network server 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 114. To this end, the social network server 122maintains and accesses an entity graph 306 (as shown in FIG. 3) withinthe database 120. Examples of functions and services supported by thesocial network server 122 include the identification of other users ofthe messaging system 100 with which a particular user has a “friend”relationship or is “following,” and also the identification of otherentities and interests of a particular user.

With reference to the functionality provided by the face animationsynthesis AR component, as mentioned above, the portrait image utilizedby the face animation synthesis AR component may be of the user who isaccessing the messaging client or it may, instead, be of a userdifferent from the user who is accessing the messaging client. Theidentification, by the social network server 122, of other users of themessaging system 100, with which a particular user has a “friend”relationship, can be used to determine the identification of a furtheruser, instead of the user who is accessing the messaging client, whoseportrait image is to be used by the face animation synthesis ARcomponent.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 112. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the sever-side by the application servers 112. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, and an augmentation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 114. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. In a furtherexample, a collection may include content, which was generated using oneor more AR components, including the face animation synthesis ARcomponent that may include content captured by a camera augmented usinga media content object modified using a previously captured and storedimage of the user. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client 104.

The collection management system 204 furthermore includes a curationinterface 212 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface212 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages).

Additionally, the collection management system 204 employs machinevision (or image recognition technology) and content rules toautomatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 206 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent, which may be associated with a message. For example, theaugmentation system 206 provides functions related to the generation andpublishing of media overlays for messages processed by the messagingsystem 100. The media overlays may be stored in the database 120 andaccessed through the database server 118.

With reference to the face animation synthesis AR component, the mediaoverlay associated with the face animation synthesis AR component is amodified source frame or a modified source video, generated by executingthe embedder machine learning model and the generator machine learningmode, as discussed herein. As explained above, the face animationsynthesis AR component, when loaded in a messaging client that receivesinput from a user, accesses a portrait image of the user (the user whois accessing the messaging client or a user different from the user whois accessing the messaging client), accesses a source frame, andexecutes the face animation synthesis process described above. Theresult of the face animation synthesis process, a source frame modifiedsuch that the face depicted in the modified source frame has thelikeness of the face from the portrait image, but the facial expressionand the color and lighting from the source frame, is presented in thecamera view user interface of the messaging client. In some examples,the face animation synthesis AR component performs the face animationsynthesis process with respect to the frames of a source video togenerate a modified source video, in which the actor's face in the videoappears to perform the same facial movements as the actor's face in thesource video, but has the likeness of the face from the portrait image.Example operations performed by the augmentation system 206,illustrating some of the functionality provided by the face animationsynthesis AR component, are described with reference to FIG. 6 furtherbelow.

In some examples, the augmentation system 206 is configured to provideaccess to AR components that can be implemented using a programminglanguage suitable for app development, such as, e.g., JavaScript or Javaand that are identified in the messaging server system by respective ARcomponent identifiers. An AR component may include or reference variousimage processing operations corresponding to an image modification,filter, media overlay, transformation, and the like. These imageprocessing operations can provide an interactive experience of areal-world environment, where objects, surfaces, backgrounds, lightingetc., captured by a digital image sensor or a camera, are enhanced bycomputer-generated perceptual information. In this context an ARcomponent comprises the collection of data, parameters, and other assetsneeded to apply a selected augmented reality experience to an image or avideo feed.

In some embodiments, an AR component includes modules configured tomodify or transform image data presented within a graphical userinterface (GUI) of a client device in some way. For example, complexadditions or transformations to the content images may be performedusing AR component data, such as adding rabbit ears to the head of aperson in a video clip, adding floating hearts with background coloringto a video clip, altering the proportions of a person's features withina video clip, or many numerous other such transformations. This includesboth real-time modifications that modify an image as it is capturedusing a camera associated with a client device and then displayed on ascreen of the client device with the AR component modifications, as wellas modifications to stored content, such as video clips in a gallerythat may be modified using AR components.

Various augmented reality functionality that may be provided by an ARcomponent include detection of objects (e.g. faces, hands, bodies, cats,dogs, surfaces, objects, etc.), tracking of such objects as they leave,enter, and move around the field of view in video frames, and themodification or transformation of such objects as they are tracked. Invarious embodiments, different methods for achieving suchtransformations may be used. For example, some embodiments may involvegenerating a 3D mesh model of the object or objects, and usingtransformations and animated textures of the model within the video toachieve the transformation. In other embodiments, tracking of points onan object may be used to place an image or texture, which may be twodimensional or three dimensional, at the tracked position. In stillfurther embodiments, neural network analysis of video frames may be usedto place images, models, or textures in content (e.g. images or framesof video). AR component data thus refers to both to the images, models,and textures used to create transformations in content, as well as toadditional modeling and analysis information needed to achieve suchtransformations with object detection, tracking, and placement.

As stated above, an example of an AR component is the face animationsynthesis AR component that, when loaded in a messaging client thatreceives input from a user, accesses a portrait image of a user,accesses a source frame, and executes the face animation synthesisprocess described above. In some examples, the face animation synthesisAR component is configured to utilize face detection technology toderive a face object from an image. Examples of face detectiontechnology include Viola-Jones feature based object detection frameworkand deep learning methods, such as “Multi-Task Cascaded ConvolutionalNeural Network,” or MTCNN.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 120 of the messaging server system 108,according to certain examples. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302 is described below with reference to FIG. 4.

An entity table 304 stores entity data, and is linked (e.g.,referentially) to an entity graph 306 and profile data 308. Entities forwhich records are maintained within the entity table 304 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 306 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example. With referenceto the functionality provided by the face animation synthesis ARcomponent, the entity graph 306 stores information that can be used, incases where the face animation synthesis AR component is configured topermit using a portrait image of a user other than that of the usercontrolling the associated client device for modifying the target mediacontent object, to determine a further profile that is connected to theprofile representing the user controlling the associated client device.

The profile data 308 stores multiple types of profile data about aparticular entity. The profile data 308 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 308 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

With reference to the functionality provided by the face animationsynthesis AR component, the profile data 308 stores the portrait imageof a user or a reference to the portrait image. The portrait image isprovided by the user represented by an associated profile. The portraitimage can used by the face animation synthesis AR component when theface animation synthesis AR component is loaded in the messaging client104, as described above.

The database 120 also stores augmentation data in an augmentation table310. The augmentation data is associated with and applied to videos (forwhich data is stored in a video table 314) and images (for which data isstored in an image table 316). In some examples, the augmentation datais used by various AR components, including the face animation synthesisAR component. An example of augmentation data is a source frame or asource video, which may be associated with a face animation synthesis ARcomponent and used to generate an associated AR experience for a user,as described above.

Another example of augmentation data is augmented reality (AR) toolsthat can be used in AR components to effectuate image transformations.Image transformations include real-time modifications, which modify animage (e.g., a video frame) as it is captured using a digital imagesensor of a client device 102. The modified image is displayed on ascreen of the client device 102 with the modifications. AR tools mayalso be used to apply modifications to stored content, such as videoclips or still images stored in a gallery. In a client device 102 withaccess to multiple AR tools, a user can apply different AR tools (e.g.,by engaging different AR components configured to utilize different ARtools) to a single video clip to see how the different AR tools wouldmodify the same video clip. For example, multiple AR tools that applydifferent pseudorandom movement models can be applied to the samecaptured content by selecting different AR tools for the same capturedcontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by a digital image sensor of a camera provided with a clientdevice 102 would modify the captured data. Such data may simply bedisplayed on the screen and not stored in memory, or the contentcaptured by digital image sensor may be recorded and stored in memorywith or without the modifications (or both). A messaging client 104 canbe configured to include a preview feature that can show howmodifications produced by different AR tools will look, within differentwindows in a display at the same time. This can, for example, permit auser to view multiple windows with different pseudorandom animationspresented on a display at the same time.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshused in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various embodiments, any combinationof such modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

A story table 312 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 304). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory. In some examples, the story table 312 stores one or more imagesor videos that were created using the face animation synthesis ARcomponent.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 314 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 316 storesimage data associated with messages for which message data is stored inthe entity table 304. The entity table 304 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 316 and the video table 314.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server114. The content of a particular message 400 is used to populate themessage table 302 stored within the database 120, accessible by themessaging server 114. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 112. The content of a message 400, in someexamples, includes an image or a video that was created using the faceanimation synthesis AR component. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 316.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 314.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image into        within the message image payload 406, or a specific video in the        message video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 312) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the Client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 316.Similarly, values within the message video payload 408 may point to datastored within a video table 314, values stored within the messageaugmentations 412 may point to data stored in an augmentation table 310,values stored within the message story identifier 418 may point to datastored in a story table 312, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 304.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral). The content of an ephemeral message 502, in some examples,includes an image or a video that was created using the face animationsynthesis AR component.

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client 104. In one example,an ephemeral message 502 is viewable by a receiving user for up to amaximum of 10 seconds, depending on the amount of time that the sendinguser specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and creation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 510, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one example, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for a timeperiod specified by the group duration parameter 508. In a furtherexample, a certain ephemeral message 502 may expire, within the contextof ephemeral message group 504, based on a group participation parameter510. Note that a message duration parameter 506 may still determine theduration of time for which a particular ephemeral message 502 isdisplayed to a receiving user, even within the context of the ephemeralmessage group 504. Accordingly, the message duration parameter 506determines the duration of time that a particular ephemeral message 502is displayed to a receiving user, regardless of whether the receivinguser is viewing that ephemeral message 502 inside or outside the contextof an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 510. For example, when a sending user hasestablished a group participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message group 504 when either the groupparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a creator of a particular ephemeral message group504 may specify an indefinite group duration parameter 508. In thiscase, the expiration of the group participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message group504 will determine when the ephemeral message group 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage group 504, with a new group participation parameter 510,effectively extends the life of an ephemeral message group 504 to equalthe value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage group 504 to no longer be displayed within a user interface ofthe messaging client 104. Similarly, when the ephemeral timer system 202determines that the message duration parameter 506 for a particularephemeral message 502 has expired, the ephemeral timer system 202 causesthe messaging client 104 to no longer display an indicium (e.g., an iconor textual identification) associated with the ephemeral message 502.

FIG. 6 is a flowchart 600 for providing an augmented reality experienceutilizing face animation synthesis. In one example embodiment, some orall processing logic resides at the client device 102 of FIG. 1 and/orat the messaging server system 108 of FIG. 1. The method 600 commencesat operation 610, when an augmentation system accesses a portrait imageand a source frame. The portrait image comprises a target face objectrepresenting a user and the source frame comprises a source face objectrepresenting an actor. The source frame may be a frame from a pluralityof frames of a source video, in which the respective face objects in theplurality of frames represent the actor acting out a scene, e.g., bytalking, laughing or expressing emotions such as joy or grief. Atoperation 620, the augmentation system generates an embeddingrepresenting facial features from the target face object by executing anembedder machine learning model. The embedder machine learning model istrained in such a way that it produces embedding that is color neutralin that it lacks representation of color and lighting of the target faceobject. The training of the embedder machine learning model to generatea color neutral embedding representing facial features is performed byusing a training dataset of training face images and, also, a set ofcolor source images. For each training face image from the trainingdataset, the training process randomly selects an image from the set ofcolor source images and generates an input face image, using therandomly selected image from the set of color source images and thetraining face image. The generated input face image has expression andfacial features from a face object of the training face image and hascolor and lighting from a face object of the randomly selected imagefrom the set of color source images. The generated input face image isused as input for executing the embedder machine learning model togenerate an embedding representing facial features from the face objectin the training face image. In some examples, the generating of theinput image comprises determining a training face set of landmarksencoding a facial expression from a face object in a training face imagefrom the training dataset, determining a color source face set oflandmarks encoding a facial expression of a color source face object ina randomly selected image from the set of color source images, warpingthe randomly selected image by modifying the color source face set oflandmarks to match the training face set of landmarks, generatingrespective pyramid representations of the warped randomly selected imageand the training face image, and using the respective pyramidrepresentations to derive the input image, a face object in the inputimage having color and lighting distinct from a color and lighting ofthe face object in the training face image. As explained above, theusing of the respective pyramid representations to derive the inputimage comprises modifying the pyramid representation of the trainingface image by replacing the smallest level of the pyramid representationof the training face image with the smallest level of the pyramidrepresentation of the warped randomly selected image, and reconstructingthe input image from the modified the pyramid representation of thetraining face image. The respective pyramid representations, in oneexample, are Laplacian pyramid representations, where the smallest levelof the pyramid representations correspond to 1/16 resolution of anassociated image.

The embedding representing facial features from the target face objectgenerated by executing the embedder machine learning model is used asinput in the generator machine learning model. At operation 630, theaugmentation system executes a generator machine learning model tomodify the source frame by replacing the source face object with a newface object, where the new face object includes the facial features fromthe target face object, a facial expression from the source face object,and color and lighting from the source face object. At operation 640,the augmentation system causes presentation of the modified source frameon a display device. In the examples where a source frame is a framefrom a plurality of frames of a source video, in which the respectiveface objects in the plurality of frames represent the actor acting out ascene, the modified source frame is from a plurality of frames of amodified source video, and the augmentation system causes presentationof the modified source video on a display device.

FIG. 7 illustrates an example representation 700 of a portrait image 710depicting a user, an image 720 depicting an actor, and an image 730showing a face resulting from a face animation synthesis process. As canbe seen in FIG. 7, the image 730 has the likeness of a face 712 shown inthe portrait image 710, (e.g., the shape and size of the nose, mouth,eyes and eyebrows, distances between the eyes, and the facial hair), buthas the expression of a face 722 in the image 720 (e.g., the directionof the gaze and the parted lips), as well as the head and the body ofthe actor in the image 720. A face 732 resulting from a face animationsynthesis process, shown in the image 730, has a mouth interior 734 thesame as a mouth interior 724 from the actor's face shown in the image720.

FIG. 8 illustrates an example representation 800 of facial landmarks 810that identify respective positions of facial features that determine afacial expression. The facial landmarks 810 indicate features such asthe position and orientation of the mouth and eyebrows, the position andorientation of the pupils that determine the direction of the gaze, andso on.

As explained above, the face animation synthesis methodologies describedherein may be made accessible to users of a messaging system. In amessaging system for exchanging data over a network, an augmentedreality component, referred to as a face animation synthesis ARcomponent, is configured to modify a target media content object, suchas a source frame, using face animation synthesis techniques describedherein. In some examples, a messaging system causes presentation of acamera view interface at a client device. The camera view interfaceincludes output of a digital image sensor of a camera of the clientdevice and, also, includes a user selectable element representing theaugmented reality component, wherein the executing of the generatormachine learning model to modify the source frame by replacing thesource face object with a new face object that includes the facialfeatures from the target face object, a facial expression from thesource face object, and color and lighting from the source face object,is in response to detecting activation of the user selectable elementrepresenting the augmented reality component. The modified source ispresented in the camera view interface at the client device, as shown inFIG. 9 and FIG. 10.

An example of a camera view user interface 900 displaying the output ofa digital image sensor of the camera in area 910 (an image 920 of a userin front of the camera) and a modified source frame 922, which isdisplayed in the camera view user interface as overlaid over a portionof the output of a digital image sensor of the camera, is shown in FIG.9. Shown in FIG. 9 is a user selectable element 930 actionable tocapture an image by the camera or to start and stop video recording. Thegraphics 940 indicates that the loaded AR component is the faceanimation synthesis AR component that can perform a modification basedon a previously captured and stored image of a user (a portrait image)and overlay it over the area 910 of the camera view user interface 900.A user selectable element 950 represents another AR component, which canbe loaded in response to a detected interaction of a user with the userselectable element 950.

FIG. 10 is a diagrammatic representation of a camera view user interface1000 displaying a modified source frame 1010 instead of the output of adigital image sensor of the camera.

As stated above, while the face animation synthesis process has beendescribed in the context of a messaging system, the methodologiesdescribed herein can be used beneficially in various other environments,such as entertainment shows, computer games, video conversations and soon.

Machine Architecture

FIG. 11 is a diagrammatic representation of the machine 1100 withinwhich instructions 1108 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1100to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1108 may cause the machine 1100to execute any one or more of the methods described herein. Theinstructions 1108 transform the general, non-programmed machine 1100into a particular machine 1100 programmed to carry out the described andillustrated functions in the manner described. The machine 1100 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1100 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1100 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1108, sequentially or otherwise,that specify actions to be taken by the machine 1100. Further, whileonly a single machine 1100 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1108 to perform any one or more of themethodologies discussed herein. The machine 1100, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1100 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1100 may include processors 1102, memory 1104, andinput/output I/O components 1138, which may be configured to communicatewith each other via a bus 1140. In an example, the processors 1102(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) Processor, a Complex Instruction Set Computing (CISC)Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), aRadio-Frequency Integrated Circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 1106and a processor 1110 that execute the instructions 1108. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.11 shows multiple processors 1102, the machine 1100 may include a singleprocessor with a single-core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 1104 includes a main memory 1112, a static memory 1114, and astorage unit 1116, both accessible to the processors 1102 via the bus1140. The main memory 1104, the static memory 1114, and storage unit1116 store the instructions 1108 embodying any one or more of themethodologies or functions described herein. The instructions 1108 mayalso reside, completely or partially, within the main memory 1112,within the static memory 1114, within machine-readable medium 1118within the storage unit 1116, within at least one of the processors 1102(e.g., within the Processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1100.

The I/O components 1138 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1138 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1138 mayinclude many other components that are not shown in FIG. 11. In variousexamples, the I/O components 1138 may include user output components1124 and user input components 1126. The user output components 1124 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1126 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1138 may include biometriccomponents 1128, motion components 1130, environmental components 1132,or position components 1134, among a wide array of other components. Forexample, the biometric components 1128 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1130 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1132 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 1134 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1138 further include communication components 1136operable to couple the machine 1100 to a network 1120 or devices 1122via respective coupling or connections. For example, the communicationcomponents 1136 may include a network interface Component or anothersuitable device to interface with the network 1120. In further examples,the communication components 1136 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1122 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 636 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 636 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1136, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1112, static memory 1114, andmemory of the processors 1102) and storage unit 1116 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1108), when executedby processors 1102, cause various operations to implement the disclosedexamples.

The instructions 1108 may be transmitted or received over the network1120, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1136) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 608 maybe transmitted or received using a transmission medium via a coupling(e.g., a peer-to-peer coupling) to the devices 1122.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1104 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method comprising: training a neural network to generate color neutral embeddings representing facial features for respective input images, the training comprising converting each image in a set of training face images into a color neutral face image using a randomly selected color source image prior to using the color neutral face image as input to the neural network; accessing a portrait image comprising a portrait face object and a frame image comprising a frame face object; and using the trained neural network, generating a modified frame image by replacing the frame face object in the frame image with a new face object, the new face object including facial features from the portrait face object, a facial expression from the frame face object, and color and lighting from the frame face object.
 2. The method of claim 1, wherein the converting of an image in the set of training face images into a color neutral face image comprises: determining a training face set of landmarks encoding a facial expression from a face object in the image; determining a color source face set of landmarks encoding a facial expression of a color frame face object in the randomly selected color source image; generating a warped image by modifying the color source face set of landmarks to match the training face set of landmarks; generating respective pyramid representations of the warped image and the training face image; and using the respective pyramid representations to derive the color neutral face image.
 3. The method of claim 2, wherein the respective pyramid representations are Laplacian pyramid representations.
 4. The method of claim 2, wherein the using of the respective pyramid representations to derive the color neutral face image: modifying the pyramid representation of the training face image by replacing the smallest level of the pyramid representation of the training face image with the smallest level of the pyramid representation of the warped image; and constructing the color neutral face image from the modified the pyramid representation of the training face image.
 5. The method of claim 4, wherein the smallest level of the pyramid representations correspond to 1/16 resolution of an associated image.
 6. The method of claim 1, comprising causing presentation of the modified frame image on a display device.
 7. The method of claim 1, wherein the frame image is from a plurality of frames of a source video and the modified frame image is from a plurality of frames of a modified source video.
 8. The method of claim 1, comprising: in a messaging system for exchanging data over a network, configuring an augmented reality component to modify a target media content object; and causing presentation of a camera view interface at a client device, the camera view interface including output of a digital image sensor of a camera of with the client device and including a user selectable element representing the augmented reality component, wherein the using of the trained neural network is in response to detecting activation of the user selectable element representing the augmented reality component.
 9. The method of claim 8, comprising overlaying at least a portion of the modified frame image over a portion of the output of the digital image sensor of the camera.
 10. The method of claim 8, wherein the portrait image is associated with a user profile representing a user in the messaging system.
 11. A system comprising: one or more processors; and a non-transitory computer readable storage medium comprising instructions that when executed by the one or processors cause the one or more processors to perform operations comprising: training a neural network to generate color neutral embeddings representing facial features for respective input images, the training comprising converting each image in a set of training face images into a color neutral face image using a randomly selected color source image prior to using the color neutral face image as input to the neural network; accessing a portrait image comprising a portrait face object and a frame image comprising a frame face object; and using the trained neural network, generating a modified frame image by replacing the frame face object in the frame image with a new face object, the new face object including facial features from the portrait face object, a facial expression from the frame face object, and color and lighting from the frame face object.
 12. The system of claim 11, wherein the converting of an image in the set of training face images into a color neutral face image comprises: determining a training face set of landmarks encoding a facial expression from a face object in the image; determining a color source face set of landmarks encoding a facial expression of a color frame face object in the randomly selected color source image; generating a warped image by modifying the color source face set of landmarks to match the training face set of landmarks; generating respective pyramid representations of the warped image and the training face image; and using the respective pyramid representations to derive the color neutral face image.
 13. The system of claim 12, wherein the respective pyramid representations are Laplacian pyramid representations.
 14. The system of claim 12, wherein the using of the respective pyramid representations to derive the color neutral face image: modifying the pyramid representation of the training face image by replacing the smallest level of the pyramid representation of the training face image with the smallest level of the pyramid representation of the warped image; and constructing the color neutral face image from the modified the pyramid representation of the training face image.
 15. The system of claim 14, wherein the smallest level of the pyramid representations correspond to 1/16 resolution of an associated image.
 16. The system of claim 11, wherein the operations caused by instructions executed by the one or processors further include causing presentation of the modified frame image on a display device.
 17. The system of claim 11, wherein the frame image is from a plurality of frames of a source video and the modified frame image is from a plurality of frames of a modified source video.
 18. The system of claim 11, comprising: in a messaging system for exchanging data over a network, configuring an augmented reality component to modify a target media content object; and causing presentation of a camera view interface at a client device, the camera view interface including output of a digital image sensor of a camera of with the client device and including a user selectable element representing the augmented reality component, wherein the using of the trained neural network is in response to detecting activation of the user selectable element representing the augmented reality component.
 19. The system of claim 18, comprising overlaying at least a portion of the modified frame image over a portion of the output of the digital image sensor of the camera.
 20. A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising: training a neural network to generate color neutral embeddings representing facial features for respective input images, the training comprising converting each image in a set of training face images into a color neutral face image using a randomly selected color source image prior to using the color neutral face image as input to the neural network; accessing a portrait image comprising a portrait face object and a frame image comprising a frame face object; and using the trained neural network, generating a modified frame image by replacing the frame face object in the frame image with a new face object, the new face object including facial features from the portrait face object, a facial expression from the frame face object, and color and lighting from the frame face object. 